Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification

作     者：Alexis Carlier Sebastien Dandrifosse Benjamin Dumont Benoit Mercatoris Alexis Carlier;Sébastien Dandrifosse;Benjamin Dumont;Beno?t Mercatoris

作者机构：Biosystems Dynamics and ExchangesTERRA Teaching and Research CentreGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium Plant SciencesTERRA Teaching and Research CentreGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium 

出 版 物：《Plant Phenomics》 (植物表型组学（英文）)

年 卷 期：2022年第4卷第1期

页      面：409-418页

核心收录：

学科分类：0710[理学-生物学] 080202[工学-机械电子工程] 08[工学] 09[农学] 0816[工学-测绘科学与技术] 0901[农学-作物学] 0802[工学-机械工程] 

基　　金：funded by the Agriculture, Natural Resources and Environment Research Direction of the Public Service of Wallonia (Belgium) project D31-1385 PHENWHEAT the National Fund of Belgium Fonds de la Recherche Scientifique-FNRS (FRIA grant) 

主　　题：Wheat heading maturity 

摘      要：The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different *** deep learning algorithms appear as promising tools to accurately detect ears in a wide diversity of ***,they remain complicated to implement and necessitate a huge training *** paper is aimed at proposing an easy and quick to train and robust alternative to segment wheat ears from heading to maturity growth stage.